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Practical course

Govern AI with method, evidence and responsibility

A progressive journey from experimental AI use to a controlled framework: inventory, risks, responsibilities, checks, documentation and human oversight.

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6-module program

Each module combines principles, control questions and reusable deliverables.

1

Fundamentals and use cases

Identify AI systems, their objectives, users and the decisions they influence.

2

Risk mapping

Assess impacts, data, bias, robustness, security, explainability and external dependencies.

3

Roles and responsibilities

Define ownership, independent validation, operations, compliance and escalation mechanisms.

4

Lifecycle and checks

Govern design, testing, production deployment, monitoring, change, incidents and retirement.

5

Human oversight

Build decision points, alert thresholds, appeal routes and evidence of human oversight.

6

Governance documentation

Assemble the system record, risk register, validations, indicators, change log and action plan.

At the end of the course

You will learn to assess use cases, assign responsibilities, choose suitable checks and build governance documentation understandable to business, risk, compliance and audit teams.

Insurance and finance-focused approach

Examples cover pricing, reserving, ALM, anomaly detection, business assistants and report generation.

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